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This data collection was created to study agenda-setting and alternative specification in the federal government. It concentrates on two federal policy areas, health and transportation, but the theories generated in the research may be quite widely applicable beyond those two areas. The aim of the work was not to study how issues are decided in some authoritative process like a congressional vote, but instead to study how issues get to be issues in the first place, how items rise and fall on the governmental agenda, and how the alternatives from which choices are made are generated.

The results of the study were published in John W. Kingdon, Agendas, Alternatives, and Public Policies (First Edition, Little Brown, 1984; Second Edition, HarperCollins, 1995; Longman Classics in Political Science Edition, Longman, 2003; Updated Second Edition, with Epilogue on Health Care Reform, Longman, 2011). The study's methods are described in detail in the Appendix to that book, and are included as part of the documentation for this data collection.

The major data source is a set of interviews that John Kingdon conducted in four waves (the summers of 1976, 1977, 1978, and 1979), with well-informed respondents either in the federal government (both congressional and executive) or involved in health or transportation policy around the federal government (e.g., lobbyists, journalists, academics, consultants). "Elite and specialized" interviews, to use Lewis Dexter's terminology (see Elite and Specialized Interviewing, Northwestern University Press, 1969), are conducted differently than standard survey research interviewing. The idea is to have a two-way conversation with a well-informed and highly involved respondent, rather than strict question and response. As such, the list of questions used was not a hard-and-fast interview schedule or questionnaire, but a kind of guide. The questions were not always asked in the same order, and indeed, not all of the questions were always asked. Question wording may have varied slightly from one interview to another. Various ad hoc probes were inserted as they seemed appropriate. Sometimes in this sort of interview, the interviewer makes a statement rather than asking a question. Still, the central questions were usually asked in roughly the same wording. Thus, when the interview write-up says "Q1," that is the first question in the standard list of questions used.

Interviews were not taped or otherwise recorded verbatim, since the principal investigator firmly believed that, with these sorts of respondents, taping dampened their ability and willingness to be candid. The principal investigator did not want respondents to feel that they were on the record, as respondents were accustomed to dealing with reporters, and when a microphone was in their face, they knew the encounter would be on the record. Notes were taken during the interview, and then written up immediately after; hence, the typescripts of the interviews are labeled "write-up" instead of "transcript." All 247 write-ups have a respondent identification number and the date of the interview on the top of the first page.

The principal investigator also coded the interview write-ups into quantitative data files, despite the nonrandom selection of respondents and the fluid conduct of the interviews. He did this to support quantitative judgments (e.g., "this issue was mentioned frequently in 1978 and not frequently in 1979," or "this factor was hardly ever mentioned in the interviews"). Each interview was coded by two coders, and then their judgments were combined. In addition to generic identifying information, there are two general categories of variables. One category, referred to as "global codes" in the codebook, is composed of ratings of the importance of each of several actors (e.g., mass media, president himself, interest groups, congressional staffers). The other category, referred to as "problem codes" is a coding of the problems that respondents discussed in their interviews, and is divided into health and transportation. A full description of coding procedures is contained in the data collection documentation.

Interview data are supplemented by a series of 23 case studies in health and transportation, and by some attention to other sources of data like congressional hearing records and public opinion data. In addition to various nonquantitative uses of the cases in the study, a quantitative dataset of the case studies was created. Two coders worked independently to judge each of a set of hypothesized influences in the case to be very important, somewhat important, of little importance, or not important. For example, after reading all of the materials for a given case study, a coder would rate the importance of congressional staffers as "very, somewhat, of little, or not" important. In contrast to the interviews, differences between the two coders were not resolved by a combination rule. Instead, the principal investigator and the two coders discussed and reached consensus in each instance in which there had been a disagreement. A full description of coding procedures is contained in the data collection documentation.

The COVID-19 Citizen Science (CCS) Study was launched early in the pandemic to collect patient-reported information about exposures, risk behaviors and outcomes relevant to the pandemic. The Patient-Centered Outcomes Research Institute (PCORI) funded the research team to expand recruitment into CCS using PCORnet, the National Patient-Centered Clinical Research Network, and to use the resulting data to compare the patient-reported impact of pandemic associated policies. The research team systematically collected pandemic-associated policies enacted by counties across the United States (focusing in areas where there were many CCS participants), and to do so on a weekly basis from the beginning of the pandemic using publicly available sources.

Researchers combined data from various sources to answer two primary research questions (RQ):

1. What is the comparative impact of different shelter-in-place/reopening policies, overall and in vulnerable populations, on patient-reported financial insecurity, mental health, and other subjective outcomes important to patients?
2. What is the comparative effectiveness of county-level containment and mitigation strategies at achieving timely access to COVID-19 vaccination, testing, healthcare, information and contact tracing?

The research team collected patient-reported data from the CCS study and policy data from the U.S COVID-19 County Policy (UCCP) database. Electronic health record (EHR) data were also available from some participants recruited from health systems located across 7 U.S. states who consented and authorized use of these data for the study. Data for these participants were extracted from the PCORnet Common Data Model (CDM). Additional county-level contextual variables were included in analysis.

This collection contains CCS survey data on patient-reported anxiety with county-level policies data (DS1), respondent demographics (DS2), baseline survey results (DS3), daily (DS4) and weekly (DS5) COVID-19 symptoms reports, COVID-19 vaccination surveys repeated monthly (DS6) as well as a one-time vaccination survey (DS7), and pandemic impacts check-in surveys (DS8). CDM datasets include logistic regression model outcomes to predict study enrollment among all invited participants (DS9), codes for immunizations (DS10), laboratory tests (DS11), and procedures (DS12). County-level variables are also available for years 2021 (DS13) and 2023 (DS14).

This survey was concerned with respondents' opinions of their neighborhoods, public policy issues, and racial issues. Housing discrimination, Black/White racial attitudes, and busing to achieve school integration were among the issues surveyed. Information was also collected on respondents' employment status and reasons for moving from or staying in their neighborhoods.